Profile-based short linear protein motif discovery

Hdl Handle:
http://hdl.handle.net/10147/264019
Title:
Profile-based short linear protein motif discovery
Authors:
Haslam, Niall J; Shields, Denis C
Citation:
BMC Bioinformatics. 2012 May 18;13(1):104
Issue Date:
18-May-2012
URI:
http://dx.doi.org/10.1186/1471-2105-13-104; http://hdl.handle.net/10147/264019
Abstract:
Abstract Background Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3–10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation. Results The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset. Conclusions Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods.
Item Type:
Journal Article

Full metadata record

DC FieldValue Language
dc.contributor.authorHaslam, Niall J-
dc.contributor.authorShields, Denis C-
dc.date.accessioned2013-01-02T16:06:50Z-
dc.date.available2013-01-02T16:06:50Z-
dc.date.issued2012-05-18-
dc.identifier.citationBMC Bioinformatics. 2012 May 18;13(1):104-
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2105-13-104-
dc.identifier.urihttp://hdl.handle.net/10147/264019-
dc.description.abstractAbstract Background Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3–10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation. Results The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset. Conclusions Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods.-
dc.titleProfile-based short linear protein motif discovery-
dc.typeJournal Article-
dc.language.rfc3066en-
dc.rights.holderNiall J Haslam et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2012-12-31T16:07:56Z-
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